diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..01fc7a4a --- /dev/null +++ b/.gitignore @@ -0,0 +1,24 @@ +sample_* +workspace* +*.root +*.png +venv +limit_* +limits_* +*.npz +combine_plots +zprimett_masses_* +analysis-systems-base +utils/__pycache__/__init__.cpython-39.pyc +utils/__pycache__/clients.cpython-39.pyc +utils/__pycache__/config.cpython-39.pyc +utils/__pycache__/config_training.cpython-39.pyc +utils/__pycache__/file_input.cpython-39.pyc +utils/__pycache__/file_output.cpython-39.pyc +utils/__pycache__/metrics.cpython-39.pyc +utils/__pycache__/ml.cpython-39.pyc +utils/__pycache__/rebinning.cpython-39.pyc +Snakefile_old +Snakefile_recast +final_merging_old.ipynb +final_merging_recast.ipynb diff --git a/Snakefile b/Snakefile index 7bd64d56..f68c546e 100644 --- a/Snakefile +++ b/Snakefile @@ -1,59 +1,93 @@ +import os +import json + +RECAST_DIR = "/eos/user/s/shoienko/REANA_file" +GLOBAL_MERGED = f"{RECAST_DIR}/histograms_merged.root" +GLOBAL_STAMP = "eos_sync.updated" + N_FILES_MAX_PER_SAMPLE = -1 -download_sleep = 0 + url_prefix = "root://eospublic.cern.ch//eos/opendata" -#In order to run analysis from Nebraska use this prefix -#url_prefix = "https://xrootd-local.unl.edu:1094//" -import glob -import json -import os +# To run from Nebraska, you may use: +# url_prefix = "https://xrootd-local.unl.edu:1094//" + def extract_samples_from_json(json_file): - output_files = [] + output_files = [] with open(json_file, "r") as fd: data = json.load(fd) - for sample, conditions in data.items(): for condition, details in conditions.items(): sample_name = f"{sample}__{condition}" output_files.append(sample_name) with open(f"sample_{sample_name}_paths.txt", "w") as path_file: - paths = [file_info["path"].replace("https://xrootd-local.unl.edu:1094//store/user/AGC/nanoAOD", - "root://eospublic.cern.ch//eos/opendata/cms/upload/agc/1.0.0/") for file_info in details["files"]] + paths = [ + file_info["path"].replace( + "https://xrootd-local.unl.edu:1094//store/user/AGC/nanoAOD", + "root://eospublic.cern.ch//eos/opendata/cms/upload/agc/1.0.0/" + ) + for file_info in details["files"] + ] path_file.write("\n".join(paths)) - return output_files - + + def get_file_paths(wildcards, max=N_FILES_MAX_PER_SAMPLE): - "Return list of at most MAX file paths for the given SAMPLE." - import json - import os - filepaths = [] - fd = open(f"sample_{wildcards.sample}__{wildcards.condition}_paths.txt") - filepaths = fd.read().splitlines() - fd.close() - return [f"histograms/histograms_{wildcards.sample}__{wildcards.condition}__"+filepath[38:] for filepath in filepaths][:max] + + with open(f"sample_{wildcards.sample}__{wildcards.condition}_paths.txt") as fd: + filepaths = fd.read().splitlines() + outs = [f"histograms/histograms_{wildcards.sample}__{wildcards.condition}__" + fp[38:] for fp in filepaths] + return outs if max == -1 else outs[:max] -samples = extract_samples_from_json("nanoaod_inputs.json") def get_items(json_file): - samples = [] - + """Return list of (sample, condition) tuples.""" + items = [] with open(json_file, "r") as fd: data = json.load(fd) - for sample, conditions in data.items(): for condition in conditions: - samples.append((sample, condition)) - - return samples + items.append((sample, condition)) + return items + + +# Bootstrap: prepare per-sample path lists +_ = extract_samples_from_json("nanoaod_inputs.json") +ITEMS = get_items("nanoaod_inputs.json") +EVERYTHING_MERGED_ROOTS = [f"everything_merged_{sample}__{condition}.root" for (sample, condition) in ITEMS] + +# Mass points to process with Combine +MASSES = [600] + + +# -------------------------- Workflow -------------------------- rule all: input: - "histograms_merged.root" + "histograms_merged.root", + "png_outputs/final_stack_histogram_4j1b.png", + "png_outputs/stack_4j2b_nominal.png", + "png_outputs/btagging_variations_4j1b_zprimett500.png", + "png_outputs/jet_energy_variations_4j2b_zprimett500.png", + "results/limits.json", + "results/limit_summary.txt", + "datacard_by_hand.root", + expand("combine_plots/impacts_r{m}.pdf", m=MASSES), + expand("combine_limits/limit_summary_{m}.txt", m=MASSES), + expand("combine_limits/limits_zprimett{m}.json", m=MASSES), + expand("combine_plots/likelihood_scan_m{m}.{ext}", m=MASSES, ext=["pdf","png","root"]), + expand("combine_plots/stacked_plot_shapes_fit_b_bin4j1b_m{m}.png", m=MASSES), + expand("combine_plots/stacked_plot_shapes_fit_b_bin4j2b_m{m}.png", m=MASSES), + expand("combine_plots/stacked_plot_shapes_prefit_bin4j1b_m{m}.png", m=MASSES), + expand("combine_plots/stacked_plot_shapes_prefit_bin4j2b_m{m}.png", m=MASSES), + expand("combine_plots/stacked_plot_shapes_fit_s_bin4j1b_m{m}.png", m=MASSES), + expand("combine_plots/stacked_plot_shapes_fit_s_bin4j2b_m{m}.png", m=MASSES), + GLOBAL_STAMP + rule process_sample_one_file_in_sample: container: - "docker.io/reanahub/reana-demo-agc-cmc-ttbar-coffea:1.0.0" + "reanahub/reana-demo-agc-cms-ttbar-coffea:1.0.0" resources: kubernetes_memory_limit="3700Mi" input: @@ -63,11 +97,15 @@ rule process_sample_one_file_in_sample: params: sample_name = '{sample}__{condition}' shell: - "/bin/bash -l && source fix-env.sh && python prepare_workspace.py sample_{params.sample_name}_{wildcards.filename} && papermill ttbar_analysis_reana.ipynb sample_{params.sample_name}_{wildcards.filename}_out.ipynb -p sample_name {params.sample_name} -p filename {url_prefix}{wildcards.filename} -k python3" + "/bin/bash -l && source fix-env.sh && " + "python prepare_workspace.py sample_{params.sample_name}_{wildcards.filename} && " + "papermill ttbar_analysis_reana.ipynb sample_{params.sample_name}_{wildcards.filename}_out.ipynb " + "-p sample_name {params.sample_name} -p filename {url_prefix}{wildcards.filename} -k python3" + rule process_sample: container: - "docker.io/reanahub/reana-demo-agc-cms-ttbar-coffea:1.0.0" + "reanahub/reana-demo-agc-cms-ttbar-coffea:1.0.0" resources: kubernetes_memory_limit="1850Mi" input: @@ -78,27 +116,130 @@ rule process_sample: params: sample_name = '{sample}__{condition}' shell: - "/bin/bash -l && source fix-env.sh && papermill file_merging.ipynb merged_{params.sample_name}.ipynb -p sample_name {params.sample_name} -k python3" + "/bin/bash -l && source fix-env.sh && " + "papermill file_merging.ipynb merged_{params.sample_name}.ipynb " + "-p sample_name {params.sample_name} -k python3" + rule merging_histograms: container: - "docker.io/reanahub/reana-demo-agc-cms-ttbar-coffea:1.0.0" + "reanahub/reana-demo-agc-cms-ttbar-coffea:1.0.0" resources: kubernetes_memory_limit="1850Mi" input: - "everything_merged_ttbar__nominal.root", - "everything_merged_ttbar__ME_var.root", - "everything_merged_ttbar__PS_var.root", - "everything_merged_ttbar__scaleup.root", - "everything_merged_ttbar__scaledown.root", - "everything_merged_single_top_s_chan__nominal.root", - "everything_merged_single_top_t_chan__nominal.root", - "everything_merged_single_top_tW__nominal.root", - "everything_merged_wjets__nominal.root", + EVERYTHING_MERGED_ROOTS, "final_merging.ipynb" output: "histograms_merged.root" + params: + recast_dir = RECAST_DIR, + global_merged = GLOBAL_MERGED + shell: + r''' + set -e + /bin/bash -l && source fix-env.sh + if [ -d "{params.recast_dir}" ]; then + TGT="{params.global_merged}" + else + TGT="histograms_merged.root" + fi + export TARGET_ROOT="$TGT" + papermill final_merging.ipynb result_notebook.ipynb -k python3 -p target_root "$TGT" + if [ "$TGT" != "histograms_merged.root" ]; then + cp -f "$TGT" histograms_merged.root || true + fi + python - <<'PY' +import os, time, sys +for _ in range(60): + if os.path.exists("histograms_merged.root") and os.path.getsize("histograms_merged.root") > 0: + sys.exit(0) + time.sleep(1) +print("histograms_merged.root not found after wait", file=sys.stderr) +sys.exit(1) +PY + ''' + + +rule sync_to_eos: + container: + "reanahub/reana-demo-agc-cms-ttbar-coffea:1.0.0" + input: + "histograms_merged.root" + output: + GLOBAL_STAMP + params: + recast_dir = RECAST_DIR, + global_merged = GLOBAL_MERGED + shell: + r''' + set -e + if [ -d "{params.recast_dir}" ]; then + printf "%s\n" "$(date) OK: symlink -> {params.global_merged}" > "{output}" + else + printf "%s\n" "$(date) LOCAL ONLY: symlink -> histograms_merged.local.root" > "{output}" + fi + ''' + + +rule final_stack_histogram: + container: + "reanahub/reana-demo-agc-cms-ttbar-coffea:1.0.0" + input: + "histograms_merged.root", + "plot_final_stack.py" + output: + "png_outputs/final_stack_histogram_4j1b.png", + "png_outputs/stack_4j2b_nominal.png", + "png_outputs/btagging_variations_4j1b_zprimett500.png", + "png_outputs/jet_energy_variations_4j2b_zprimett500.png" shell: - "/bin/bash -l && source fix-env.sh && papermill final_merging.ipynb result_notebook.ipynb -k python3" + "/bin/bash -l && source fix-env.sh && python3 plot_final_stack.py" - \ No newline at end of file + +rule compute_limit: + """ + Run limit extraction using cabinetry. + cabinetry_config.yml now points to the local symlink path; no env needed. + """ + container: + "reanahub/reana-demo-agc-cms-ttbar-coffea:1.0.0" + input: + "histograms_merged.root", + "cabinetry_config.yml", + "cabinetry_fit_limit.py" + output: + "results/limits.json", + "results/limit_summary.txt", + "workspace.json" + shell: + "/bin/bash -l && source fix-env.sh && python3 cabinetry_fit_limit.py" + + +rule combine: + container: + "gitlab-registry.cern.ch/cms-cloud/combine-standalone:latest" + input: + "histograms_merged.root", + "statistical_inference.py", + "make_mirrored_down.py", + "datacard_by_hand.txt", + "combine_scripts/plot1DScan.py", + "combine_scripts/postFitPlot_new.py" + output: + "datacard_by_hand.root", + expand("combine_plots/likelihood_scan_m{m}.{ext}", m=MASSES, ext=["pdf","png","root"]), + expand("combine_plots/impacts_r{m}.pdf", m=MASSES), + expand("combine_plots/stacked_plot_shapes_fit_b_bin4j1b_m{m}.png", m=MASSES), + expand("combine_plots/stacked_plot_shapes_fit_b_bin4j2b_m{m}.png", m=MASSES), + expand("combine_plots/stacked_plot_shapes_prefit_bin4j1b_m{m}.png", m=MASSES), + expand("combine_plots/stacked_plot_shapes_prefit_bin4j2b_m{m}.png", m=MASSES), + expand("combine_plots/stacked_plot_shapes_fit_s_bin4j1b_m{m}.png", m=MASSES), + expand("combine_plots/stacked_plot_shapes_fit_s_bin4j2b_m{m}.png", m=MASSES), + expand("combine_limits/limit_summary_{m}.txt", m=MASSES), + expand("combine_limits/limits_zprimett{m}.json", m=MASSES) + shell: + r""" + set -e + /bin/bash -l && source fix-env.sh + python3 statistical_inference.py + """ diff --git a/cabinetry_config.yml b/cabinetry_config.yml new file mode 100644 index 00000000..8ac46868 --- /dev/null +++ b/cabinetry_config.yml @@ -0,0 +1,111 @@ +General: + Measurement: "CMS_ttbar" + POI: "zprimett600_norm" + HistogramFolder: "histograms" + InputPath: "histograms_merged.root:{RegionPath}_{SamplePath}{VariationPath}" + VariationPath: "_nominal" + +Regions: + - Name: "4j1b CR" + RegionPath: "4j1b" + Variable: "$H_T$ [GeV]" + Binning: [110, 130, 150, 170, 190, 210, 230, 250, 270, 290, 310, 330, 350, 370, 390, 410, 430, 450, 470, 490, 510, 530, 550] + - Name: "4j2b SR" + RegionPath: "4j2b" + Variable: "$m_{bjj}$ [GeV]" + Binning: [110, 130, 150, 170, 190, 210, 230, 250, 270, 290, 310, 330, 350, 370, 390, 410, 430, 450, 470, 490, 510, 530, 550] + +Samples: + - Name: "Pseudodata" + SamplePath: "pseudodata" + Data: True + + - Name: "ttbar" + SamplePath: "ttbar" + + - Name: "W+jets" + SamplePath: "wjets" + + - Name: "single top, s-channel" + SamplePath: "single_top_s_chan" + + - Name: "single top, t-channel" + SamplePath: "single_top_t_chan" + + - Name: "tW" + SamplePath: "single_top_tW" + + - Name: "zprimett600" + SamplePath: "zprimett600" + +Systematics: + - Name: "ME variation" + Type: "NormPlusShape" + Up: { VariationPath: "_ME_var" } + Down: { Symmetrize: True } + Samples: "ttbar" + + - Name: "PS variation" + Type: "NormPlusShape" + Up: { VariationPath: "_PS_var" } + Down: { Symmetrize: True } + Samples: "ttbar" + + - Name: "ttbar scale variations" + Type: "NormPlusShape" + Up: { VariationPath: "_scaleup" } + Down: { VariationPath: "_scaledown" } + Samples: "ttbar" + + - Name: "Jet energy scale" + Type: "NormPlusShape" + Up: { VariationPath: "_pt_scale_up" } + Down: { Symmetrize: True } + Samples: ["ttbar", "W+jets", "single_top_s_chan", "single_top_t_chan", "single_top_tW", "zprimett600"] + + - Name: "Jet energy resolution" + Type: "NormPlusShape" + Up: { VariationPath: "_pt_res_up" } + Down: { Symmetrize: True } + Samples: ["ttbar", "W+jets", "single_top_s_chan", "single_top_t_chan", "single_top_tW", "zprimett600"] + + - Name: "b-tag NP 1" + Type: "NormPlusShape" + Up: { VariationPath: "_btag_var_0_up" } + Down: { VariationPath: "_btag_var_0_down" } + Samples: ["ttbar", "W+jets", "single_top_s_chan", "single_top_t_chan", "single_top_tW", "zprimett600"] + + - Name: "b-tag NP 2" + Type: "NormPlusShape" + Up: { VariationPath: "_btag_var_1_up" } + Down: { VariationPath: "_btag_var_1_down" } + Samples: ["ttbar", "W+jets", "single_top_s_chan", "single_top_t_chan", "single_top_tW", "zprimett600"] + + - Name: "b-tag NP 3" + Type: "NormPlusShape" + Up: { VariationPath: "_btag_var_2_up" } + Down: { VariationPath: "_btag_var_2_down" } + Samples: ["ttbar", "W+jets", "single_top_s_chan", "single_top_t_chan", "single_top_tW", "zprimett600"] + + - Name: "b-tag NP 4" + Type: "NormPlusShape" + Up: { VariationPath: "_btag_var_3_up" } + Down: { VariationPath: "_btag_var_3_down" } + Samples: ["ttbar", "W+jets", "single_top_s_chan", "single_top_t_chan", "single_top_tW", "zprimett600"] + + - Name: "W+jets scale variations" + Type: "NormPlusShape" + Up: { VariationPath: "_scale_var_up" } + Down: { VariationPath: "_scale_var_down" } + Samples: "W+jets" + + - Name: "Luminosity" + Type: "Normalization" + Up: { Normalization: 0.03 } + Down: { Normalization: -0.03 } + +NormFactors: + - Name: "zprimett600_norm" + Samples: "zprimett600" + Nominal: 1.0 + Bounds: [-100, 100] diff --git a/cabinetry_fit_limit.py b/cabinetry_fit_limit.py new file mode 100644 index 00000000..8240af5d --- /dev/null +++ b/cabinetry_fit_limit.py @@ -0,0 +1,220 @@ +import os +import sys +import json +import types +import pathlib +import math +import numpy as np + +import pyhf +import cabinetry +from cabinetry import configuration, templates, workspace, model_utils + + +def _to_py(x): + import numpy as _np + if x is None: + return None + if isinstance(x, _np.ndarray): + return x.tolist() + if isinstance(x, (_np.floating, _np.integer, _np.bool_)): + return x.item() + if isinstance(x, (list, tuple)): + return [_to_py(v) for v in x] + if isinstance(x, dict): + return {k: _to_py(v) for k, v in x.items()} + return x + + +def set_pyhf_backend(): + backend = pyhf.tensor.numpy_backend(precision="64b") + default_minimizer = None + try: + default_minimizer = pyhf.optimize.minuit_optimizer() + print("[INFO] iminuit optimizer is available.") + except Exception as e: + print(f"[WARN] iminuit not available ({e}). Will use SciPy default optimizer.") + + try: + if default_minimizer is not None: + pyhf.set_backend(backend, default_minimizer=default_minimizer) + print("[INFO] Using iminuit via set_backend(default_minimizer=...).") + else: + pyhf.set_backend(backend) + print("[INFO] Using SciPy optimizer (default).") + except TypeError: + pyhf.set_backend(backend) + print("[WARN] Old pyhf: 'default_minimizer' not supported; using SciPy.") + + +def try_import_hist(): + try: + import hist # noqa: F401 + return hist + except Exception as e: + print(f"[WARN] 'hist' not available ({e}). AGC rebinning may be skipped.") + return None + + +def try_import_agc_rebinning_module(): + repo_root = pathlib.Path(__file__).resolve().parent + rebin_path = repo_root / "utils" / "rebinning.py" + if not rebin_path.exists(): + print(f"[WARN] {rebin_path} not found. AGC rebinning will be skipped.") + return None + try: + import importlib.util + utils_pkg = types.ModuleType("utils") + utils_pkg.__path__ = [str(rebin_path.parent)] + sys.modules.setdefault("utils", utils_pkg) + spec = importlib.util.spec_from_file_location("utils.rebinning", str(rebin_path)) + mod = importlib.util.module_from_spec(spec) + sys.modules["utils.rebinning"] = mod + spec.loader.exec_module(mod) # type: ignore[attr-defined] + return mod + except Exception as e: + print(f"[WARN] Failed to import AGC rebinning: {e}") + return None + + +def build_templates_with_optional_rebin(cfg, merge_factor=2): + used_rebinning = False + hist_mod = try_import_hist() + agc_rebin = try_import_agc_rebinning_module() + if hist_mod is not None and agc_rebin is not None: + try: + rebinning_router = agc_rebin.get_cabinetry_rebinning_router( + cfg, rebinning=slice(110j, None, hist_mod.rebin(merge_factor)) + ) + print(f"[INFO] Building templates with AGC rebinning router (merge {merge_factor}->1)...") + templates.build(cfg, router=rebinning_router) + used_rebinning = True + except Exception as e: + print(f"[WARN] AGC rebinning failed ({e}). Building without router...") + templates.build(cfg) + else: + print("[INFO] Building templates without rebinning...") + templates.build(cfg) + + try: + templates.postprocess(cfg) + except Exception as e: + print(f"[WARN] templates.postprocess skipped ({e})") + + return used_rebinning + + +def write_results(poi, observed, expected, used_rebinning, note, ok=True): + os.makedirs("results", exist_ok=True) + observed_py = _to_py(observed) + expected_py = _to_py(expected) + expected_median_py = None + if expected is not None and hasattr(expected, "__len__") and len(expected) >= 3: + expected_median_py = _to_py(expected[2]) + with open("results/limit_summary.txt", "w") as f: + f.write(f"POI: {poi}\n") + f.write(f"Observed 95% CL: {observed_py}\n") + f.write(f"Expected (median) 95% CL: {expected_median_py}\n") + f.write(f"AGC_rebinning_applied: {used_rebinning}\n") + f.write(f"Note: {note}\n") + f.write(f"Status: {'OK' if ok else 'FALLBACK'}\n") + with open("results/limits.json", "w") as f: + json.dump( + { + "poi": poi, + "observed_95": observed_py, + "expected_median_95": expected_median_py, + "expected_bands_95": expected_py, + "AGC_rebinning_applied": used_rebinning, + "note": note, + "status": "OK" if ok else "FALLBACK", + }, + f, + indent=2, + ) + print("[OK] results written: results/limit_summary.txt, results/limits.json") + + +def robust_observed_limit_scan(model, data, cls_target=0.05, mu_min=0.0, mu_max=50.0, n_steps=200): + bounds = model.config.suggested_bounds() + par_bounds = bounds + init_pars = model.config.suggested_init() + for i in range(n_steps + 1): + mu = mu_min + (mu_max - mu_min) * (i / n_steps) + try: + cls = float(pyhf.infer.hypotest(mu, data, model, init_pars=init_pars, par_bounds=par_bounds)) + except pyhf.exceptions.FailedMinimization as e: + print(f"[WARN] hypotest FailedMinimization at mu={mu:.3f}: {e}") + continue + except Exception as e: + print(f"[WARN] hypotest error at mu={mu:.3f}: {e}") + continue + if cls <= cls_target: + return mu + return None + + +def main(): + print("[INFO] configuring pyhf backend ...") + set_pyhf_backend() + + print("[INFO] loading cabinetry_config.yml ...") + cfg = configuration.load("cabinetry_config.yml") + + # Collect and build + templates.collect(cfg) + used_rebinning = build_templates_with_optional_rebin(cfg, merge_factor=2) + + # Build ws and run limit + print("[INFO] building workspace.json ...") + ws = workspace.build(cfg) + workspace.save(ws, "workspace.json") + print("[OK] workspace.json written. AGC_rebinning_applied =", used_rebinning) + + model, data = model_utils.model_and_data(ws) + poi = model.config.poi_name + + lo, hi = model.config.suggested_bounds()[model.config.poi_index] + import math as _m + lo = max(0.0, float(lo)) if _m.isfinite(lo) else 0.0 + hi = float(hi) if _m.isfinite(hi) and hi > 0 else 100.0 + bracket = (lo, hi) + print(f"[INFO] limit bracket for '{poi}': {bracket}") + + try: + print("[INFO] running cabinetry.fit.limit (first try) ...") + res = cabinetry.fit.limit( + model, data, poi_name=poi, bracket=bracket, strategy=1, maxiter=20000, + ) + observed = getattr(res, "observed_limit", getattr(res, "upper_limit", None)) + expected = getattr(res, "expected_limit", None) + write_results(poi, observed, expected, used_rebinning, "standard cabinetry.fit.limit", ok=True) + return + except Exception as e: + print(f"[WARN] first try failed: {e}") + + try: + print("[INFO] retry: stronger rebinning (merge 4->1) ...") + cfg2 = configuration.load("cabinetry_config.yml") + templates.collect(cfg2) + used_rebin2 = build_templates_with_optional_rebin(cfg2, merge_factor=4) + ws2 = workspace.build(cfg2) + workspace.save(ws2, "workspace.json") + model, data = model_utils.model_and_data(ws2) + res = cabinetry.fit.limit( + model, data, poi_name=poi, bracket=bracket, strategy=1, maxiter=20000, + ) + observed = getattr(res, "observed_limit", getattr(res, "upper_limit", None)) + expected = getattr(res, "expected_limit", None) + write_results(poi, observed, expected, used_rebin2, "cabinetry.fit.limit (merge 4->1)", ok=True) + return + except Exception as e: + print(f"[WARN] second try failed: {e}") + + print("[INFO] fallback: brute observed CLs scan ...") + mu_obs = robust_observed_limit_scan(model, data, cls_target=0.05, mu_min=0.0, mu_max=max(hi, 50.0), n_steps=300) + write_results(poi, mu_obs, None, used_rebinning, "fallback brute CLs scan (observed only); expected bands not computed", ok=False) + + +if __name__ == "__main__": + main() diff --git a/combine_limits/plot_limits.py b/combine_limits/plot_limits.py new file mode 100644 index 00000000..59096e50 --- /dev/null +++ b/combine_limits/plot_limits.py @@ -0,0 +1,178 @@ +#!/usr/bin/env python3 +import os, re, glob, json, argparse +import numpy as np +import matplotlib.pyplot as plt + +def parse_summary(path): + poi = obs = exp_med = None + try: + with open(path) as f: + for line in f: + s = line.strip() + if s.startswith("POI:"): + poi = s.split(":", 1)[1].strip() + elif s.startswith("Observed 95% CL:"): + try: obs = float(s.split(":", 1)[1]) + except: obs = None + elif s.startswith("Expected (median) 95% CL:"): + try: exp_med = float(s.split(":", 1)[1]) + except: exp_med = None + except FileNotFoundError: + pass + return poi, obs, exp_med + +def mass_from(fname, poi): + m = re.search(r"limit_summary[_-](\d+)", os.path.basename(fname)) + if m: return int(m.group(1)) + if poi: + m = re.search(r"zprimett(\d+)", poi) + if m: return int(m.group(1)) + return None + +def _read_collectlimits_json(path): + + with open(path) as f: + obj = json.load(f) + + if isinstance(obj, dict) and len(obj) == 1 and isinstance(next(iter(obj.values())), dict): + obj = next(iter(obj.values())) + return obj + +def load_bands(mass, dirs): + """Зчитує [−2σ, −1σ, median, +1σ, +2σ] з CollectLimits JSON.""" + cands = [] + for d in dirs: + d = d.strip() + cands += glob.glob(os.path.join(d, f"limits_zprimett{mass}.json")) + cands += glob.glob(os.path.join(d, f"limits_{mass}.json")) + for p in cands: + try: + obj = _read_collectlimits_json(p) + + keys = ("exp-2","exp-1","exp0","exp+1","exp+2") + if all(k in obj for k in keys): + return [float(obj["exp-2"]), float(obj["exp-1"]), float(obj["exp0"]), + float(obj["exp+1"]), float(obj["exp+2"])] + except Exception: + pass + return None + +def load_obs_exp_from_json(mass, dirs): + """Повертає (obs, exp_median) з CollectLimits JSON, якщо доступно.""" + cands = [] + for d in dirs: + d = d.strip() + cands += glob.glob(os.path.join(d, f"limits_zprimett{mass}.json")) + cands += glob.glob(os.path.join(d, f"limits_{mass}.json")) + for p in cands: + try: + obj = _read_collectlimits_json(p) + obs = float(obj["obs"]) if "obs" in obj else None + exp0 = float(obj["exp0"]) if "exp0" in obj else None + if (obs is not None) or (exp0 is not None): + return obs, exp0 + except Exception: + pass + return None, None + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--summaries_glob", default="combine_limits/limit_summary_*.txt") + ap.add_argument("--search_json_dirs", default="combine_limits,results,.") + ap.add_argument("--xlabel", default=r"$m_{Z'}$ [GeV]") + ap.add_argument("--ylabel", default=r"95% CL upper limit on $\mu$") + ap.add_argument("--out", default="limits_summary.png") + ap.add_argument("--title", default="") + ap.add_argument("--logy", action="store_true") + args = ap.parse_args() + + search_dirs = [d for d in args.search_json_dirs.split(",") if d] + + rows = [] + + summary_paths = sorted(glob.glob(args.summaries_glob)) + for p in summary_paths: + poi, obs, exp_med = parse_summary(p) + m = mass_from(p, poi) + if m is None: + continue + + if obs is None or exp_med is None: + j_obs, j_exp = load_obs_exp_from_json(m, search_dirs) + if obs is None: obs = j_obs + if exp_med is None: exp_med = j_exp + bands = load_bands(m, search_dirs) + rows.append((m, obs, exp_med, bands)) + + + if not rows: + + jsons = [] + for d in search_dirs: + jsons += glob.glob(os.path.join(d, "limits_zprimett*.json")) + jsons += glob.glob(os.path.join(d, "limits_*.json")) + for jp in sorted(set(jsons)): + + m = None + mobj = re.search(r"zprimett(\d+)", os.path.basename(jp)) + if not mobj: + mobj = re.search(r"limits_(\d+)", os.path.basename(jp)) + if mobj: + m = int(mobj.group(1)) + if m is None: + continue + j_obs, j_exp = load_obs_exp_from_json(m, search_dirs) + bands = load_bands(m, search_dirs) + rows.append((m, j_obs, j_exp, bands)) + + if not rows: + raise SystemExit("No inputs found (neither summaries nor JSON).") + + rows.sort(key=lambda x: x[0]) + masses = np.array([r[0] for r in rows], float) + obs = np.array([r[1] if r[1] is not None else np.nan for r in rows], float) + exp_med = np.array([r[2] if r[2] is not None else np.nan for r in rows], float) + bands = [r[3] for r in rows] + have_bands = any(b is not None for b in bands) + + plt.figure(figsize=(8, 5.2)) + + if have_bands: + exp_m2 = np.array([b[0] if b else np.nan for b in bands], float) + exp_m1 = np.array([b[1] if b else np.nan for b in bands], float) + exp_p1 = np.array([b[3] if b else np.nan for b in bands], float) + exp_p2 = np.array([b[4] if b else np.nan for b in bands], float) + plt.fill_between(masses, exp_m2, exp_p2, alpha=0.6, color="yellow", label=r"Exp. $\pm2\sigma$") + plt.fill_between(masses, exp_m1, exp_p1, alpha=0.8, color="lime", label=r"Exp. $\pm1\sigma$") + + + plt.plot(masses, exp_med, "--", color="black", lw=2, label="Expected") + plt.plot(masses, obs, "-", color="black", lw=2, label="Observed") + + plt.xlabel(args.xlabel) + plt.ylabel(args.ylabel) + if args.title: plt.title(args.title) + if args.logy: + plt.yscale("log") + finite = np.concatenate([np.nan_to_num(obs, nan=np.inf), + np.nan_to_num(exp_med, nan=np.inf)]) + if have_bands: + finite = np.concatenate([finite, + np.nan_to_num(exp_m2, nan=np.inf), + np.nan_to_num(exp_p2, nan=np.inf)]) + finite = finite[np.isfinite(finite) & (finite > 0)] + if finite.size: + plt.ylim(finite.min()/1.8, finite.max()*1.8) + + h, l = plt.gca().get_legend_handles_labels() + order = [i for name in [r"Exp. $\pm2\sigma$", r"Exp. $\pm1\sigma$", "Expected", "Observed"] if name in l for i in [l.index(name)]] + if order: + plt.legend([h[i] for i in order], [l[i] for i in order], loc="best", frameon=False) + + plt.grid(True, which="both", ls=":", alpha=0.4) + plt.tight_layout() + plt.savefig(args.out, dpi=200) + print(f"[OK] saved {args.out}") + +if __name__ == "__main__": + main() diff --git a/combine_scripts/plot1DScan.py b/combine_scripts/plot1DScan.py new file mode 100644 index 00000000..dd082bf5 --- /dev/null +++ b/combine_scripts/plot1DScan.py @@ -0,0 +1,299 @@ +#!/usr/bin/env python3 +from __future__ import absolute_import, print_function + +import argparse +import json +import math +import os.path +from functools import partial + +from six.moves import range + +import HiggsAnalysis.CombinedLimit.util.plotting as plot +import ROOT + +ROOT.PyConfig.IgnoreCommandLineOptions = True +ROOT.gROOT.SetBatch(ROOT.kTRUE) + +plot.ModTDRStyle(width=700, l=0.13) +ROOT.gStyle.SetNdivisions(510, "XYZ") +ROOT.gStyle.SetMarkerSize(0.7) + +NAMECOUNTER = 0 + + +def read(scan, param, files, ycut): + goodfiles = [f for f in files if plot.TFileIsGood(f)] + limit = plot.MakeTChain(goodfiles, "limit") + graph = plot.TGraphFromTree(limit, param, "2*deltaNLL", "quantileExpected > -1.5") + graph.SetName(scan) + graph.Sort() + plot.RemoveGraphXDuplicates(graph) + plot.RemoveGraphYAbove(graph, ycut) + # graph.Print() + return graph + + +def Eval(obj, x, params): + return obj.Eval(x[0]) + + +def BuildScan(scan, param, files, color, yvals, ycut): + graph = read(scan, param, files, ycut) + bestfit = None + for i in range(graph.GetN()): + if graph.GetY()[i] == 0.0: + bestfit = graph.GetX()[i] + graph.SetMarkerColor(color) + spline = ROOT.TSpline3("spline3", graph) + global NAMECOUNTER + func_method = partial(Eval, spline) + func = ROOT.TF1("splinefn" + str(NAMECOUNTER), func_method, graph.GetX()[0], graph.GetX()[graph.GetN() - 1], 1) + func._method = func_method + NAMECOUNTER += 1 + func.SetLineColor(color) + func.SetLineWidth(3) + assert bestfit is not None + crossings = {} + cross_1sig = None + cross_2sig = None + other_1sig = [] + other_2sig = [] + val = None + val_2sig = None + for yval in yvals: + crossings[yval] = plot.FindCrossingsWithSpline(graph, func, yval) + for cr in crossings[yval]: + cr["contains_bf"] = cr["lo"] <= bestfit and cr["hi"] >= bestfit + for cr in crossings[yvals[0]]: + if cr["contains_bf"]: + val = (bestfit, cr["hi"] - bestfit, cr["lo"] - bestfit) + cross_1sig = cr + else: + other_1sig.append(cr) + if len(yvals) > 1: + for cr in crossings[yvals[1]]: + if cr["contains_bf"]: + val_2sig = (bestfit, cr["hi"] - bestfit, cr["lo"] - bestfit) + cross_2sig = cr + else: + other_2sig.append(cr) + else: + val_2sig = (0.0, 0.0, 0.0) + cross_2sig = cross_1sig + return { + "graph": graph, + "spline": spline, + "func": func, + "crossings": crossings, + "val": val, + "val_2sig": val_2sig, + "cross_1sig": cross_1sig, + "cross_2sig": cross_2sig, + "other_1sig": other_1sig, + "other_2sig": other_2sig, + } + + +parser = argparse.ArgumentParser() + +parser.add_argument("main", help="Main input file for the scan") +parser.add_argument("--y-cut", type=float, default=7.0, help="Remove points with y > y-cut") +parser.add_argument("--y-max", type=float, default=8.0, help="y-axis maximum") +parser.add_argument("--output", "-o", help="output name without file extension", default="scan") +parser.add_argument("--POI", help="use this parameter of interest", default="r") +parser.add_argument("--translate", default=None, help="json file with POI name translation") +parser.add_argument("--main-label", default="Observed", type=str, help="legend label for the main scan") +parser.add_argument("--main-color", default=1, type=int, help="line and marker color for main scan") +parser.add_argument( + "--others", + nargs="*", + help="add secondary scans processed as main: FILE:LABEL:COLOR", +) +parser.add_argument("--breakdown", help="do quadratic error subtraction using --others") +parser.add_argument("--logo", default="CMS") +parser.add_argument("--logo-sub", default="Internal") +args = parser.parse_args() + +print("--------------------------------------") +print(args.output) +print("--------------------------------------") + +fixed_name = args.POI +if args.translate is not None: + with open(args.translate) as jsonfile: + name_translate = json.load(jsonfile) + if args.POI in name_translate: + fixed_name = name_translate[args.POI] + +yvals = [1.0, 4.0] + + +main_scan = BuildScan(args.output, args.POI, [args.main], args.main_color, yvals, args.y_cut) + +other_scans = [] +other_scans_opts = [] +if args.others is not None: + for oargs in args.others: + splitargs = oargs.split(":") + other_scans_opts.append(splitargs) + other_scans.append( + BuildScan( + args.output, + args.POI, + [splitargs[0]], + int(splitargs[2]), + yvals, + args.y_cut, + ) + ) + + +canv = ROOT.TCanvas(args.output, args.output) +pads = plot.OnePad() +main_scan["graph"].SetMarkerColor(1) +main_scan["graph"].Draw("AP") + +axishist = plot.GetAxisHist(pads[0]) + +axishist.SetMaximum(args.y_max) +axishist.GetYaxis().SetTitle("- 2 #Delta ln L") +axishist.GetXaxis().SetTitle("%s" % fixed_name) + +new_min = axishist.GetXaxis().GetXmin() +new_max = axishist.GetXaxis().GetXmax() +mins = [] +maxs = [] +for other in other_scans: + mins.append(other["graph"].GetX()[0]) + maxs.append(other["graph"].GetX()[other["graph"].GetN() - 1]) + +if len(other_scans) > 0: + if min(mins) < main_scan["graph"].GetX()[0]: + new_min = min(mins) - (main_scan["graph"].GetX()[0] - new_min) + if max(maxs) > main_scan["graph"].GetX()[main_scan["graph"].GetN() - 1]: + new_max = max(maxs) + (new_max - main_scan["graph"].GetX()[main_scan["graph"].GetN() - 1]) + axishist.GetXaxis().SetLimits(new_min, new_max) + +for other in other_scans: + if args.breakdown is not None: + other["graph"].SetMarkerSize(0.4) + other["graph"].Draw("PSAME") + +line = ROOT.TLine() +line.SetLineColor(16) +# line.SetLineStyle(7) +for yval in yvals: + plot.DrawHorizontalLine(pads[0], line, yval) + if len(other_scans) == 0: + for cr in main_scan["crossings"][yval]: + if cr["valid_lo"]: + line.DrawLine(cr["lo"], 0, cr["lo"], yval) + if cr["valid_hi"]: + line.DrawLine(cr["hi"], 0, cr["hi"], yval) + +main_scan["func"].Draw("same") +for other in other_scans: + if args.breakdown is not None: + other["func"].SetLineStyle(2) + other["func"].SetLineWidth(2) + other["func"].Draw("SAME") + + +box = ROOT.TBox( + axishist.GetXaxis().GetXmin(), + 0.625 * args.y_max, + axishist.GetXaxis().GetXmax(), + args.y_max, +) +box.Draw() +pads[0].GetFrame().Draw() +pads[0].RedrawAxis() + +crossings = main_scan["crossings"] +val_nom = main_scan["val"] +val_2sig = main_scan["val_2sig"] + +textfit = "%s = %.3f{}^{#plus %.3f}_{#minus %.3f}" % ( + fixed_name, + val_nom[0], + val_nom[1], + abs(val_nom[2]), +) + + +pt = ROOT.TPaveText(0.59, 0.82 - len(other_scans) * 0.08, 0.95, 0.91, "NDCNB") +pt.AddText(textfit) + +if args.breakdown is None: + for i, other in enumerate(other_scans): + textfit = "#color[%s]{%s = %.3f{}^{#plus %.3f}_{#minus %.3f}}" % ( + other_scans_opts[i][2], + fixed_name, + other["val"][0], + other["val"][1], + abs(other["val"][2]), + ) + pt.AddText(textfit) + + +if args.breakdown is not None: + pt.SetX1(0.50) + if len(other_scans) >= 3: + pt.SetX1(0.19) + pt.SetX2(0.88) + pt.SetY1(0.66) + pt.SetY2(0.82) + breakdown = args.breakdown.split(",") + v_hi = [val_nom[1]] + v_lo = [val_nom[2]] + for other in other_scans: + v_hi.append(other["val"][1]) + v_lo.append(other["val"][2]) + assert len(v_hi) == len(breakdown) + textfit = "%s = %.3f" % (fixed_name, val_nom[0]) + for i, br in enumerate(breakdown): + if i < (len(breakdown) - 1): + if abs(v_hi[i + 1]) > abs(v_hi[i]): + print("ERROR SUBTRACTION IS NEGATIVE FOR %s HI" % br) + hi = 0.0 + else: + hi = math.sqrt(v_hi[i] * v_hi[i] - v_hi[i + 1] * v_hi[i + 1]) + if abs(v_lo[i + 1]) > abs(v_lo[i]): + print("ERROR SUBTRACTION IS NEGATIVE FOR %s LO" % br) + lo = 0.0 + else: + lo = math.sqrt(v_lo[i] * v_lo[i] - v_lo[i + 1] * v_lo[i + 1]) + else: + hi = v_hi[i] + lo = v_lo[i] + textfit += "{}^{#plus %.3f}_{#minus %.3f}(%s)" % (hi, abs(lo), br) + pt.AddText(textfit) + + +pt.SetTextAlign(11) +pt.SetTextFont(42) +pt.Draw() + +plot.DrawCMSLogo(pads[0], args.logo, args.logo_sub, 11, 0.085, 0.035, 1.2, cmsTextSize=1.0) + +legend_l = 0.69 +if len(other_scans) > 0: + legend_l = legend_l - len(other_scans) * 0.04 +legend = ROOT.TLegend(0.15, legend_l, 0.45, 0.78, "", "NBNDC") +if len(other_scans) >= 3: + legend = ROOT.TLegend(0.46, 0.83, 0.95, 0.93, "", "NBNDC") + legend.SetNColumns(2) + +legend.AddEntry(main_scan["func"], args.main_label, "L") +for i, other in enumerate(other_scans): + legend.AddEntry(other["func"], other_scans_opts[i][1], "L") +legend.Draw() + +save_graph = main_scan["graph"].Clone() +save_graph.GetXaxis().SetTitle("%s = %.3f %+.3f/%+.3f" % (fixed_name, val_nom[0], val_nom[2], val_nom[1])) +outfile = ROOT.TFile(args.output + ".root", "RECREATE") +outfile.WriteTObject(save_graph) +outfile.Close() +canv.Print(".pdf") +canv.Print(".png") \ No newline at end of file diff --git a/combine_scripts/postFitPlot_new.py b/combine_scripts/postFitPlot_new.py new file mode 100644 index 00000000..9441b8b3 --- /dev/null +++ b/combine_scripts/postFitPlot_new.py @@ -0,0 +1,156 @@ +#!/usr/bin/env python3 +from __future__ import absolute_import +import argparse +import ctypes +import ROOT +import HiggsAnalysis.CombinedLimit.util.plotting as plot + +ROOT.PyConfig.IgnoreCommandLineOptions = True +ROOT.gROOT.SetBatch(True) +plot.ModTDRStyle() + +def get(dir_, name): + obj = dir_.Get(name) + return obj if obj else None + +def first_existing(dir_, names): + for n in names: + o = get(dir_, n) + if o: return o + return None + +def graph_max(g): + # Compute max from points, avoiding g.GetHistogram() (which needs Draw) + if not g: return 0.0 + n = g.GetN() + y = ctypes.c_double(0.0) + x = ctypes.c_double(0.0) + ymax = 0.0 + for i in range(n): + g.GetPoint(i, x, y) + ymax = max(ymax, y.value + g.GetErrorYhigh(i)) + return ymax + +def axis_edges(h1): + ax = h1.GetXaxis() + nb = ax.GetNbins() + # Collect low-edges + last upper edge + edges = [ax.GetBinLowEdge(i) for i in range(1, nb+1)] + edges.insert(0, ax.GetXmin()) + edges.append(ax.GetXmax()) + # De-dup & sort (ROOT axes sometimes duplicate xmin as first low-edge) + edges = sorted(set(edges)) + return edges + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--input_file", required=True) + ap.add_argument("--shape_type", required=True, choices=["shapes_prefit","shapes_fit_b","shapes_fit_s"]) + ap.add_argument("--region", required=True) + ap.add_argument("--extra_suffix", default="") + args = ap.parse_args() + + f = ROOT.TFile.Open(args.input_file) + if not f or f.IsZombie(): + raise RuntimeError("cannot open %s" % args.input_file) + + d = f.Get(f"{args.shape_type}/{args.region}") + if not d: + raise RuntimeError("missing directory %s/%s" % (args.shape_type, args.region)) + + h_bkg = first_existing(d, ["total_background"]) + h_sig = first_existing(d, ["total_signal"]) + g_dat = first_existing(d, ["data", "data_obs", "data_obs_"]) + + if not h_bkg: + raise RuntimeError("total_background not found") + # Signal may be absent in fit_b / prefit — tolerate it + if not h_sig: + h_sig = h_bkg.Clone("empty_signal") + h_sig.Reset("ICES") + if not g_dat: + # also fine: no data graph + pass + + # Styles + h_bkg.SetFillColor(ROOT.TColor.GetColor(100,192,232)) + h_bkg.SetLineColor(ROOT.kAzure+2) + h_sig.SetFillColor(ROOT.kRed) + h_sig.SetLineColor(ROOT.kRed+1) + + # Stack + hs = ROOT.THStack("hs", "") + hs.Add(h_bkg, "hist") + if h_sig.Integral() > 0: + hs.Add(h_sig, "hist") + + # Uncertainty band on (bkg+sig) + h_tot = h_bkg.Clone("h_tot"); h_tot.Add(h_sig) + h_err = h_tot.Clone("h_err") + for i in range(1, h_err.GetNbinsX()+1): + be = h_bkg.GetBinError(i) + 1e-3 + se = h_sig.GetBinError(i) + 1e-3 + h_err.SetBinError(i, (be*be + se*se)**0.5) + h_err.SetFillColorAlpha(12, 0.30) + h_err.SetMarkerSize(0) + h_err.SetLineWidth(0) + + # Optional re-range (use SetRangeUser for TH1) + xmin, xmax = h_bkg.GetXaxis().GetXmin(), h_bkg.GetXaxis().GetXmax() + # If you want hard limits like 110–550, uncomment: + # xmin, xmax = 110.0, 550.0 + for h in (h_bkg, h_sig, h_tot, h_err): + h.GetXaxis().SetRangeUser(xmin, xmax) + + # Prepare a re-centered data graph (to avoid manual bin_edges off-by-one) + new_g = None + if g_dat: + new_g = ROOT.TGraphAsymmErrors(g_dat.GetN()) + nb = h_bkg.GetNbinsX() + for i in range(g_dat.GetN()): + x = ctypes.c_double(0.0) + y = ctypes.c_double(0.0) + g_dat.GetPoint(i, x, y) + # Clamp to histogram binning + bin_idx = min(i+1, nb) + xc = h_bkg.GetXaxis().GetBinCenter(bin_idx) + hw = 0.5*(h_bkg.GetXaxis().GetBinUpEdge(bin_idx) - h_bkg.GetXaxis().GetBinLowEdge(bin_idx)) + new_g.SetPoint(i, xc, y.value) + new_g.SetPointError(i, hw, hw, g_dat.GetErrorYlow(i), g_dat.GetErrorYhigh(i)) + new_g.SetMarkerStyle(20) + new_g.SetMarkerSize(1.0) + new_g.SetLineColor(ROOT.kBlack) + + # Y range + ymax_h = h_tot.GetMaximum() + h_err.GetBinError(h_err.GetMaximumBin()) + ymax_g = graph_max(new_g if new_g else g_dat) + ymax = max(ymax_h, ymax_g) if (new_g or g_dat) else ymax_h + c = ROOT.TCanvas("c","c",900,800) + c.SetMargin(0.12,0.04,0.12,0.06) + hs.SetMinimum(0.0) + hs.SetMaximum(max(1.0, ymax*1.3)) + hs.Draw("HIST") + hs.GetXaxis().SetTitle(args.region) + hs.GetYaxis().SetTitle("Events") + h_err.Draw("E2 SAME") + if new_g: + new_g.Draw("P SAME") + + # Legend + leg = ROOT.TLegend(0.60,0.70,0.90,0.91,"","NBNDC") + leg.AddEntry(h_bkg, "Background", "F") + if h_sig.Integral() > 0: + leg.AddEntry(h_sig, "Z'→tt", "F") + leg.AddEntry(h_err, "Total uncertainty", "F") + if new_g: leg.AddEntry(new_g, "Data", "PE") + leg.SetBorderSize(0); leg.SetFillStyle(0); leg.Draw() + + # Save + out_png = f"combine_plots/stacked_plot_{args.shape_type}_{args.region}{args.extra_suffix}.png" + out_pdf = f"combine_plots/stacked_plot_{args.shape_type}_{args.region}{args.extra_suffix}.pdf" + c.Print(out_png) + c.Print(out_pdf) + print("Saved:", out_png, "and", out_pdf) + +if __name__ == "__main__": + main() diff --git a/datacard_by_hand.txt b/datacard_by_hand.txt new file mode 100644 index 00000000..057dba31 --- /dev/null +++ b/datacard_by_hand.txt @@ -0,0 +1,47 @@ +imax 2 +jmax * +kmax * +-------------------------------------------------------------------------------------------------------------------------------- + +shapes data_obs bin4j1b histograms_merged.root 4j1b_pseudodata_nominal +shapes data_obs bin4j2b histograms_merged.root 4j2b_pseudodata_nominal + +# generic mapping: nominal and $SYSTEMATIC{Up,Down} + +shapes * bin4j1b histograms_merged.root 4j1b_$PROCESS_nominal 4j1b_$PROCESS_$SYSTEMATIC +shapes * bin4j2b histograms_merged.root 4j2b_$PROCESS_nominal 4j2b_$PROCESS_$SYSTEMATIC +-------------------------------------------------------------------------------------------------------------------------------- + +bin bin4j1b bin4j2b +observation -1 -1 +-------------------------------------------------------------------------------------------------------------------------------- +# --- only zprimett 400–1200 kept --- +bin bin4j1b bin4j1b bin4j1b bin4j1b bin4j1b bin4j1b bin4j1b bin4j1b bin4j1b bin4j1b bin4j1b bin4j1b bin4j1b bin4j2b bin4j2b bin4j2b bin4j2b bin4j2b bin4j2b bin4j2b bin4j2b bin4j2b bin4j2b bin4j2b bin4j2b bin4j2b +process zprimett400 zprimett500 zprimett600 zprimett700 zprimett800 zprimett900 zprimett1000 zprimett1200 ttbar single_top_s_chan single_top_t_chan single_top_tW wjets zprimett400 zprimett500 zprimett600 zprimett700 zprimett800 zprimett900 zprimett1000 zprimett1200 ttbar single_top_s_chan single_top_t_chan single_top_tW wjets +process 0 -1 -2 -3 -4 -5 -6 -7 1 2 3 4 5 0 -1 -2 -3 -4 -5 -6 -7 1 2 3 4 5 +rate -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 +-------------------------------------------------------------------------------------------------------------------------------- +# Jet-related (mirrored Down from Up) +pt_scale shape 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 +pt_res shape 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + +# b-tagging (apply to all) +btag_var_0 shape 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 +btag_var_1 shape 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 +btag_var_2 shape 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 +btag_var_3 shape 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + +# ttbar modeling (only ttbar columns -> put 1 in the ttbar slot, 0 elsewhere) +ME_var shape 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 +PS_var shape 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 +scale shape 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 + +# W+jets specific (only wjets columns -> 1 there) +scale_var shape 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 + +# luminosity (applies to all) +lumi lnN 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.03 + +# MC stat per bin +bin4j1b autoMCStats 0 +bin4j2b autoMCStats 0 diff --git a/docs/ecosystem.png b/docs/ecosystem.png deleted file mode 100644 index 791aa0a1..00000000 Binary files a/docs/ecosystem.png and /dev/null differ diff --git a/docs/jetcombinations.png b/docs/jetcombinations.png deleted file mode 100644 index 2e6d21f3..00000000 Binary files a/docs/jetcombinations.png and /dev/null differ diff --git a/docs/processing_pipelines.png b/docs/processing_pipelines.png deleted file mode 100644 index 78fdeeda..00000000 Binary files a/docs/processing_pipelines.png and /dev/null differ diff --git a/docs/ttbar_labels.png b/docs/ttbar_labels.png deleted file mode 100644 index f7d25aca..00000000 Binary files a/docs/ttbar_labels.png and /dev/null differ diff --git a/figures/limit.pdf b/figures/limit.pdf new file mode 100644 index 00000000..db4220a7 Binary files /dev/null and b/figures/limit.pdf differ diff --git a/final_merging.ipynb b/final_merging.ipynb index d99a0a61..1770148c 100644 --- a/final_merging.ipynb +++ b/final_merging.ipynb @@ -6,35 +6,308 @@ "metadata": {}, "outputs": [], "source": [ + "target_root = \"histograms_merged.root\" \n", + "JSON_FILE = \"nanoaod_inputs.json\"\n", + "EMPTY_HIST_YIELD = 1.0\n", + "\n", + "import os\n", + "import json\n", + "import time\n", + "import numpy as np\n", "import uproot\n", - "import json" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + "from uproot.writing.identify import to_TH1x, to_TAxis\n", + "\n", + "# Optional env override if papermill did not inject parameters\n", + "_target_from_env = os.environ.get(\"TARGET_ROOT\")\n", + "if _target_from_env:\n", + " target_root = _target_from_env\n", + "else:\n", + " # default to EOS location\n", + " target_root = GLOBAL_MERGED\n", + "\n", + "# ------------------------- helpers -------------------------\n", + "\n", "def extract_samples_from_json(json_file):\n", - " output_files = []\n", - " with open(json_file, 'r') as fd:\n", + " \"\"\"Return list of (sample, condition) pairs encountered in the input JSON.\"\"\"\n", + " out = []\n", + " with open(json_file, \"r\") as fd:\n", " data = json.load(fd)\n", " for sample, conditions in data.items():\n", " for condition in conditions:\n", - " sample_name = f\"everything_merged_{sample}__{condition}.root\"\n", - " output_files.append(sample_name)\n", - " return output_files\n", - "json_file = \"nanoaod_inputs.json\"\n", - "# LIST_OF_FILES_PER_SAMPLE is a list of 9 files containing histograms per sample\n", - "LIST_OF_FILES_PER_SAMPLE = extract_samples_from_json(json_file)\n", - "print(LIST_OF_FILES_PER_SAMPLE)\n", - "with uproot.recreate(\"histograms_merged.root\") as f_out:\n", - " for h_file in LIST_OF_FILES_PER_SAMPLE:\n", - " with uproot.open(h_file) as f_per_sample:\n", - " for key in f_per_sample.keys(cycle=False):\n", - " value = f_per_sample[key]\n", - " f_out[key] = value" + " out.append((sample, condition))\n", + " return out\n", + "\n", + "def list_channel_keys(file_handle, process_tag):\n", + " \"\"\"Map channel name -> full histogram key for histograms ending with _{process_tag}.\"\"\"\n", + " out = {}\n", + " for key in file_handle.keys(cycle=False):\n", + " name = key.split(\";\")[0]\n", + " if name.endswith(\"_\" + process_tag):\n", + " channel = name[: -(len(process_tag) + 1)]\n", + " out[channel] = name\n", + " return out\n", + "\n", + "def h_sum_yield(h):\n", + " \"\"\"Total yield of a histogram (NaNs treated as 0).\"\"\"\n", + " vals, _ = h.to_numpy()\n", + " return float(np.nansum(vals))\n", + "\n", + "def try_variances(h):\n", + " \"\"\"Return array of variances if available, else None.\"\"\"\n", + " try:\n", + " v = h.variances()\n", + " if v is not None:\n", + " return np.asarray(v)\n", + " except Exception:\n", + " pass\n", + " return None\n", + "\n", + "def build_th1x(vals, edges, name, title, var=None):\n", + " \"\"\"Construct a ROOT TH1x payload (for uproot writing) from numpy arrays.\"\"\"\n", + " vals = np.asarray(vals, dtype=\"float64\")\n", + " edges = np.asarray(edges, dtype=\"float64\")\n", + " nbins = len(edges) - 1\n", + "\n", + " data = np.zeros(nbins + 2, dtype=\"float64\")\n", + " data[1:-1] = vals\n", + "\n", + " if var is None:\n", + " sumw2_core = vals.copy()\n", + " else:\n", + " sumw2_core = np.asarray(var, dtype=\"float64\")\n", + " sumw2 = np.zeros(nbins + 2, dtype=\"float64\")\n", + " sumw2[1:-1] = sumw2_core\n", + "\n", + " centers = 0.5 * (edges[1:] + edges[:-1])\n", + " fEntries = float(np.sum(vals))\n", + " fTsumw = float(np.sum(vals))\n", + " fTsumw2 = float(np.sum(sumw2_core))\n", + " fTsumwx = float(np.sum(vals * centers))\n", + " fTsumwx2 = float(np.sum(vals * centers**2))\n", + "\n", + " xaxis = to_TAxis(\"xaxis\", \"\", nbins, float(edges[0]), float(edges[-1]), edges)\n", + "\n", + " fMaximum = float(np.max(vals) * 1.2) if np.max(vals) > 0 else 1.0\n", + " fMinimum = 0.0\n", + " fBarOffset = 0\n", + " fBarWidth = 1\n", + "\n", + " try:\n", + " return to_TH1x(\n", + " name, title, data,\n", + " fEntries, fTsumw, fTsumw2, fTsumwx, fTsumwx2,\n", + " sumw2, xaxis,\n", + " None, None,\n", + " None,\n", + " fBarOffset, fBarWidth,\n", + " fMaximum, fMinimum,\n", + " )\n", + " except TypeError:\n", + " # Fallback for older uproot without extra args\n", + " return to_TH1x(\n", + " name, title, data,\n", + " fEntries, fTsumw, fTsumw2, fTsumwx, fTsumwx2,\n", + " sumw2, xaxis\n", + " )\n", + "\n", + "def get_title_safe(h):\n", + " \"\"\"Retrieve histogram title robustly.\"\"\"\n", + " try:\n", + " t = h.member(\"fTitle\")\n", + " try:\n", + " return t.decode() if isinstance(t, (bytes, bytearray)) else str(t)\n", + " except Exception:\n", + " return str(t)\n", + " except Exception:\n", + " try:\n", + " return h.title\n", + " except Exception:\n", + " return \"\"\n", + "\n", + "# ------------------------- gather inputs -------------------------\n", + "\n", + "items = extract_samples_from_json(JSON_FILE)\n", + "everything_roots = [f\"everything_merged_{s}__{c}.root\" for (s, c) in items]\n", + "\n", + "# Files required to produce pseudodata histograms\n", + "req_ttbar_me = \"everything_merged_ttbar__ME_var.root\"\n", + "req_ttbar_ps = \"everything_merged_ttbar__PS_var.root\"\n", + "req_wjets_no = \"everything_merged_wjets__nominal.root\"\n", + "\n", + "# ------------------------- collect all content (arrays) -------------------------\n", + "\n", + "# We first collect arrays, then build TH1x objects at the very end.\n", + "# This allows us to generate alias histograms with a correct internal name.\n", + "result_map = {} # name -> (vals, edges, var, title)\n", + "\n", + "# 1) Collect all histograms produced in this run (overwrite policy per key name, no cycles)\n", + "for h_file in everything_roots:\n", + " try:\n", + " with uproot.open(h_file) as f_in:\n", + " for k in f_in.keys(cycle=False):\n", + " name = k.split(\";\")[0]\n", + " h = f_in[k]\n", + " vals, edges = h.to_numpy()\n", + " var = try_variances(h)\n", + " title = get_title_safe(h)\n", + " result_map[name] = (vals, edges, var, title) # overwrite if duplicate name appears\n", + " except FileNotFoundError:\n", + " print(f\"[WARN] missing: {h_file}\")\n", + "\n", + "# 2) Optional: build pseudodata and add into result_map\n", + "def add_pseudodata():\n", + " if not all(p in everything_roots for p in (req_ttbar_me, req_ttbar_ps, req_wjets_no)):\n", + " return\n", + " with uproot.open(req_ttbar_me) as f_ttbar_ME, \\\n", + " uproot.open(req_ttbar_ps) as f_ttbar_PS, \\\n", + " uproot.open(req_wjets_no) as f_wjets:\n", + "\n", + " ttbar_me_map = list_channel_keys(f_ttbar_ME, \"ttbar_ME_var\")\n", + " ttbar_ps_map = list_channel_keys(f_ttbar_PS, \"ttbar_PS_var\")\n", + " wjets_map = list_channel_keys(f_wjets, \"wjets_nominal\")\n", + " common_channels = sorted(set(ttbar_me_map) & set(ttbar_ps_map) & set(wjets_map))\n", + " print(f\"[INFO] channels for pseudodata_nominal: {len(common_channels)}\")\n", + "\n", + " for channel in common_channels:\n", + " h_me = f_ttbar_ME[ttbar_me_map[channel]]\n", + " h_ps = f_ttbar_PS[ttbar_ps_map[channel]]\n", + " h_wj = f_wjets[wjets_map[channel]]\n", + "\n", + " if (\n", + " h_sum_yield(h_me) <= EMPTY_HIST_YIELD or\n", + " h_sum_yield(h_ps) <= EMPTY_HIST_YIELD or\n", + " h_sum_yield(h_wj) <= EMPTY_HIST_YIELD\n", + " ):\n", + " continue\n", + "\n", + " vals_me, edges = h_me.to_numpy()\n", + " vals_ps, edges_ps = h_ps.to_numpy()\n", + " vals_wj, edges_wj = h_wj.to_numpy()\n", + "\n", + " if not (np.allclose(edges, edges_ps) and np.allclose(edges, edges_wj)):\n", + " print(f\"[WARN] binning mismatch in {channel}, skipping pseudodata for it\")\n", + " continue\n", + "\n", + " new_vals = 0.5 * (vals_me + vals_ps) + vals_wj\n", + " var_me = try_variances(h_me)\n", + " var_ps = try_variances(h_ps)\n", + " var_wj = try_variances(h_wj)\n", + " new_var = (\n", + " 0.25 * (var_me + var_ps) + var_wj\n", + " if (var_me is not None and var_ps is not None and var_wj is not None)\n", + " else None\n", + " )\n", + " hname = f\"{channel}_pseudodata_nominal\"\n", + " htitle = \"Pseudodata = 0.5*(ttbar_ME + ttbar_PS) + wjets_nominal\"\n", + " result_map[hname] = (new_vals, edges, new_var, htitle)\n", + "\n", + "add_pseudodata()\n", + "\n", + "# 3) Add Combine-friendly aliases (Up/Down) for existing *_up/*_down keys,\n", + "# and create Up/Down for one-sided modeling (ttbar_ME_var / ttbar_PS_var).\n", + "def add_combine_aliases(res):\n", + " \"\"\"Create extra keys with Up/Down suffixes so Combine can match 4j?_PROC_$SYSTEMATIC{Up,Down}.\"\"\"\n", + " def add_alias(dst, src_tuple):\n", + " # do not overwrite if already present\n", + " if dst not in res:\n", + " res[dst] = src_tuple\n", + "\n", + " # (a) Generic conversion: ..._up -> ...Up, ..._down -> ...Down\n", + " for name, tup in list(res.items()):\n", + " if name.endswith(\"_up\"):\n", + " add_alias(name[:-3] + \"Up\", tup)\n", + " if name.endswith(\"_down\"):\n", + " add_alias(name[:-5] + \"Down\", tup)\n", + "\n", + " # (b) Special: ttbar_scaleup/scaledown -> ttbar_scaleUp/scaleDown\n", + " for name, tup in list(res.items()):\n", + " if name.endswith(\"_ttbar_scaleup\"):\n", + " add_alias(name[:-len(\"scaleup\")] + \"scaleUp\", tup)\n", + " if name.endswith(\"_ttbar_scaledown\"):\n", + " add_alias(name[:-len(\"scaledown\")] + \"scaleDown\", tup)\n", + "\n", + " # (c) One-sided modeling systematics: ttbar_ME_var / ttbar_PS_var\n", + " # We create *Up from the provided shape and *Down from the nominal of the same (channel, process).\n", + " def nominal_key_for(prefix_before_process, process):\n", + " candidate = f\"{prefix_before_process}_{process}_nominal\"\n", + " return candidate if candidate in res else None\n", + "\n", + " for name, tup in list(res.items()):\n", + " if name.endswith(\"_ttbar_ME_var\"):\n", + " prefix = name[: -len(\"_ttbar_ME_var\")]\n", + " up_key = name + \"Up\"\n", + " dn_key = name + \"Down\"\n", + " add_alias(up_key, tup)\n", + " nom = nominal_key_for(prefix, \"ttbar\")\n", + " if nom:\n", + " add_alias(dn_key, res[nom])\n", + "\n", + " if name.endswith(\"_ttbar_PS_var\"):\n", + " prefix = name[: -len(\"_ttbar_PS_var\")]\n", + " up_key = name + \"Up\"\n", + " dn_key = name + \"Down\"\n", + " add_alias(up_key, tup)\n", + " nom = nominal_key_for(prefix, \"ttbar\")\n", + " if nom:\n", + " add_alias(dn_key, res[nom])\n", + "\n", + "add_combine_aliases(result_map)\n", + "\n", + "\n", + "os.makedirs(os.path.dirname(target_root) or \".\", exist_ok=True)\n", + "\n", + "base_name, ext = os.path.splitext(os.path.basename(target_root))\n", + "dir_name = os.path.dirname(target_root)\n", + "tmp_root = os.path.join(dir_name, base_name + \".tmp\" + ext)\n", + "\n", + "# Keys we are going to write/update in this pass\n", + "names_to_update = set(result_map.keys())\n", + "\n", + "# Optional file lock to avoid concurrent writes\n", + "lock_path = os.path.join(dir_name, base_name + \".lock\")\n", + "_use_lock = True\n", + "try:\n", + " import fcntl # POSIX only\n", + "except Exception:\n", + " _use_lock = False\n", + "\n", + "def _do_merge():\n", + " with uproot.recreate(tmp_root) as f_tmp:\n", + " # Copy-through existing keys that are NOT being updated this pass\n", + " if os.path.exists(target_root):\n", + " with uproot.open(target_root) as f_old:\n", + " for k in f_old.keys(cycle=False):\n", + " name = k.split(\";\")[0]\n", + " if name in names_to_update:\n", + " continue # skip keys that will be replaced\n", + " f_tmp[name] = f_old[k]\n", + "\n", + " # Write/overwrite all new/aliased histograms (no cycles in a fresh file)\n", + " for name, (vals, edges, var, title) in result_map.items():\n", + " f_tmp[name] = build_th1x(vals, edges, name, title or name, var)\n", + "\n", + " # Atomic replace temp -> target (must be same filesystem/dir)\n", + " os.replace(tmp_root, target_root)\n", + " print(\"[OK] Wrote new file and replaced:\", target_root)\n", + "\n", + " # FS latency guard\n", + " for _ in range(20):\n", + " if os.path.exists(target_root) and os.path.getsize(target_root) > 0:\n", + " print(\"[OK] Verified target_root:\", target_root)\n", + " break\n", + " time.sleep(0.5)\n", + " else:\n", + " print(\"[WARN] target_root not visible yet:\", target_root)\n", + "\n", + "if _use_lock:\n", + " with open(lock_path, \"w\") as _lf:\n", + " fcntl.flock(_lf, fcntl.LOCK_EX)\n", + " try:\n", + " _do_merge()\n", + " finally:\n", + " fcntl.flock(_lf, fcntl.LOCK_UN)\n", + "else:\n", + " _do_merge()\n" ] } ], diff --git a/json_zprime/nanoaod_inputs_2016.json b/json_zprime/nanoaod_inputs_2016.json new file mode 100644 index 00000000..b36fe8bd --- /dev/null +++ b/json_zprime/nanoaod_inputs_2016.json @@ -0,0 +1,3568 @@ +{ + "ttbar": { + "nominal": { + "nevts_total": 295335000, + "files": [ + { + "path": "root://eospublic.cern.ch//eos/opendata/cms/mc/RunIISummer20UL16NanoAODv9/TTTo2L2Nu_TuneCP5_13TeV-powheg-pythia8/NANOAODSIM/106X_mcRun2_asymptotic_v17-v1/120000/089E6E3A-DFD1-7148-BD6F-F611D22A1B62.root", + "nevts": 136000 + }, + { + "path": 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f.Get(name) + return obj if obj else None + +def make_down(ch, proc, syst): + hNom = get(f"{ch}_{proc}_nominal") + if not hNom: + return False + hUp = get(f"{ch}_{proc}_{syst}Up") or get(f"{ch}_{proc}_{syst}_up") + if not hUp: + return False + + hDown = hNom.Clone(f"{ch}_{proc}_{syst}Down") + hDown.Add(hUp, -1.0) + hDown.Scale(2.0) + + for ib in range(1, hDown.GetNbinsX() + 1): + if hDown.GetBinContent(ib) < 0.0: + hDown.SetBinContent(ib, 0.0) + hDown.SetBinError(ib, 0.0) + + int_nom = max(hNom.Integral(), eps) + int_down = hDown.Integral() + + if int_down > eps: + hDown.Scale(int_nom / int_down) + else: + hDown = hNom.Clone(f"{ch}_{proc}_{syst}Down") + + f.WriteTObject(hDown, hDown.GetName(), "Overwrite") + return True + +created = 0 +for key in f.GetListOfKeys(): + name = key.GetName() + if not name.endswith("_nominal"): + continue + parts = name.split("_") + ch = parts[0] + proc = "_".join(parts[1:-1]) + for syst in systs: + if make_down(ch, proc, syst): + created += 1 + +print(f"Fixed/created {created} Down histograms") +f.Close() diff --git a/mass_plot.py b/mass_plot.py new file mode 100644 index 00000000..f56fdfe7 --- /dev/null +++ b/mass_plot.py @@ -0,0 +1,85 @@ +# pip install uproot matplotlib numpy +import re +import numpy as np +import uproot +import matplotlib.pyplot as plt + +ROOT_PATH = "histograms_merged.root" +CHANNELS = ["4j1b", "4j2b"] + +def collect_nominals(file, channel): + + out = {} + + keys = [k.split(";")[0] for k in file.keys()] + pat = re.compile(rf"^{re.escape(channel)}_zprimett(\d+)_nominal$") + for k in keys: + m = pat.match(k) + if not m: + continue + mass = int(m.group(1)) + values, edges = file[k].to_numpy() + out[mass] = (values, edges) + return out + +def plot_channel(ax, nominals, channel, masses, title_suffix): + + if not masses: + ax.text(0.5, 0.5, "No histograms found", + transform=ax.transAxes, ha="center") + return + + min_edge = float("inf") + max_edge = float("-inf") + + for mass in sorted(masses): + vals, edges = nominals[mass] + ax.step(edges[:-1], vals, where="post", label=f"{mass} GeV", lw=1.2) + min_edge = min(min_edge, edges[0]) + max_edge = max(max_edge, edges[-1]) + + left = max(0.0, min_edge - 50.0) + ax.set_xlim(left, max_edge) + + ax.set_xlabel(r"$H_T$ [GeV]") + ax.set_ylabel("Events") + ax.set_title(rf"Z′ → t$\bar{{t}}$ masses — {channel} {title_suffix}") + ax.legend( + title="Masses", + loc="upper right", + fontsize="small", + title_fontsize="small", + frameon=True, + framealpha=0.85, + ncol=2 + ) + + +def main(): + with uproot.open(ROOT_PATH) as f: + for ch in CHANNELS: + nom = collect_nominals(f, ch) + if not nom: + print(f"No data for {ch}") + continue + + + low_masses = [m for m in nom if 400 <= m <= 2500] + high_masses = [m for m in nom if m > 2500] + + + fig1, ax1 = plt.subplots(figsize=(6.2, 4.8)) + plot_channel(ax1, nom, ch, low_masses, "(400–2500 GeV)") + fig1.tight_layout() + fig1.savefig(f"zprimett_masses_{ch}_low.png", dpi=130) + + + fig2, ax2 = plt.subplots(figsize=(6.2, 4.8)) + plot_channel(ax2, nom, ch, high_masses, "(>2500 GeV)") + fig2.tight_layout() + fig2.savefig(f"zprimett_masses_{ch}_high.png", dpi=130) + + print(f"Saved: zprimett_masses_{ch}_low.png, zprimett_masses_{ch}_high.png") + +if __name__ == "__main__": + main() diff --git a/nanoaod_inputs.json b/nanoaod_inputs.json index fddb72df..a85bff33 100644 --- a/nanoaod_inputs.json +++ b/nanoaod_inputs.json @@ -1,3205 +1,3878 @@ { - "ttbar": { - "nominal": { - "nevts_total": 276079127, - "files": [ - { - "path": "https://xrootd-local.unl.edu:1094//store/user/AGC/nanoAOD/TT_TuneCUETP8M1_13TeV-powheg-pythia8/cmsopendata2015_ttbar_19980_PU25nsData2015v1_76X_mcRun2_asymptotic_v12_ext3-v1_00000_0000.root", - "nevts": 1334428 - }, - { - "path": "https://xrootd-local.unl.edu:1094//store/user/AGC/nanoAOD/TT_TuneCUETP8M1_13TeV-powheg-pythia8/cmsopendata2015_ttbar_19980_PU25nsData2015v1_76X_mcRun2_asymptotic_v12_ext3-v1_00000_0001.root", - "nevts": 1297266 - }, - { - "path": 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["4j1b", "4j2b"] + + +PROCESS_ORDER = [ + "single_top_tW", + "single_top_t_chan", + "single_top_s_chan", + "wjets", + "ttbar", + "zprimett500", + "zprimett600", + "zprimett700", + "zprimett800", + "zprimett900" +] + +def normalize_variation(v: str) -> str: + """Normalize variation name: + - 'nominal' kept as-is + - ...Up -> ..._up + - ...Down -> ..._down + - other strings returned unchanged + """ + if v == "nominal": + return v + if v.endswith("Up"): + return v[:-2] + "_up" + if v.endswith("Down"): + return v[:-4] + "_down" + return v + +def parse_key(key: str): + """Parse 'channel_process[_variation]' with robust process detection.""" + if "_" not in key: + return None + channel, rest = key.split("_", 1) + + # Try to split at the last '_' to separate process and variation if possible + # but also handle exact '..._nominal' + if rest.endswith("_nominal"): + proc = rest[: -len("_nominal")] + var = "nominal" + return channel, proc, var + + # If we have something like 'ttbar_scaleUp' or 'btag_var_0_up' + m = re.match(r"(.+?)_(.+)$", rest) + if m: + proc, var = m.group(1), m.group(2) + return channel, proc, normalize_variation(var) + + # Fallback: treat whole rest as process (nominal) + return channel, rest, "nominal" + +def load_all_histograms(filename: str): + """Read all TH1 from a ROOT file and organize them by (channel, process, variation).""" + file = uproot.open(filename) + histograms = {} + + for key_with_version in file.keys(): + key = key_with_version.split(";")[0] + parsed = parse_key(key) + if not parsed: + continue + + channel, proc, variation = parsed + + try: + values, edges = file[key_with_version].to_numpy() + except Exception: + continue + + h = hist.Hist.new.Var(edges, name="x").Double() + h.view(flow=False)[...] = values + + histograms.setdefault(channel, {}).setdefault(proc, {})[variation] = h + + return histograms + +def plot_stack(hist_dict, channel: str, variation: str, out_file: str, xlabel: str, title: str): + """Make a stacked plot of the backgrounds in PROCESS_ORDER for one channel & variation.""" + from hist.stack import Stack + + proc_hists = [] + labels = [] + + for proc in PROCESS_ORDER: + if proc in hist_dict and variation in hist_dict[proc]: + # light rebin example (adjust or remove as needed) + h = hist_dict[proc][variation][::hist.rebin(2)] + proc_hists.append(h) + labels.append(proc) + + if not proc_hists: + print(f"[WARN] No histograms found for channel={channel}, variation={variation}") + return + + stack = Stack(*proc_hists) + fig, ax = plt.subplots() + stack.plot(stack=True, histtype="fill", edgecolor="grey", linewidth=1, label=labels, ax=ax) + ax.legend(frameon=False) + ax.set_title(title) + ax.set_xlabel(xlabel) + ax.set_ylabel("Events") + fig.tight_layout() + fig.savefig(out_file, dpi=300) + plt.close(fig) + +def plot_variations(hist_dict): + """Example variation overlays for a single signal mass if present.""" + # pick any existing z' signal in 4j1b for b-tag variations + for mass in ["zprimett500", "zprimett600", "zprimett700", "zprimett800", "zprimett900"]: + if "4j1b" in hist_dict and mass in hist_dict["4j1b"]: + base = hist_dict["4j1b"][mass].get("nominal") + if base is not None: + fig, ax = plt.subplots() + base[120j::hist.rebin(2)].plot(label="nominal", linewidth=2, ax=ax) + for i in range(4): + var = f"btag_var_{i}_up" + if var in hist_dict["4j1b"][mass]: + hist_dict["4j1b"][mass][var][120j::hist.rebin(2)].plot( + label=f"NP {i+1}", linewidth=2, ax=ax + ) + ax.legend(frameon=False) + ax.set_xlabel(r"$H_T$ [GeV]") + ax.set_title(f"b-tagging variations (4j1b, {mass})") + fig.tight_layout() + fig.savefig(f"png_outputs/btagging_variations_4j1b_{mass}.png", dpi=300) + plt.close(fig) + break + + # jet energy variations in 4j2b + for mass in ["zprimett500", "zprimett600", "zprimett700", "zprimett800", "zprimett900"]: + if "4j2b" in hist_dict and mass in hist_dict["4j2b"]: + base = hist_dict["4j2b"][mass].get("nominal") + if base is not None: + fig, ax = plt.subplots() + base.plot(label="nominal", linewidth=2, ax=ax) + if "pt_scale_up" in hist_dict["4j2b"][mass]: + hist_dict["4j2b"][mass]["pt_scale_up"].plot(label="scale up", linewidth=2, ax=ax) + if "pt_res_up" in hist_dict["4j2b"][mass]: + hist_dict["4j2b"][mass]["pt_res_up"].plot(label="resolution up", linewidth=2, ax=ax) + ax.legend(frameon=False) + ax.set_xlabel(r"$m_{bjj}$ [GeV]") + ax.set_title(f"Jet energy variations (4j2b, {mass})") + fig.tight_layout() + fig.savefig(f"png_outputs/jet_energy_variations_4j2b_{mass}.png", dpi=300) + plt.close(fig) + break + +def find_signals(histograms, prefix="zprimett"): + """Return sorted list of processes starting with 'prefix' seen in any channel.""" + procs = set() + for ch in histograms.values(): + for p in ch.keys(): + if p.startswith(prefix): + procs.add(p) + return sorted(procs) + +if __name__ == "__main__": + all_hists = load_all_histograms(FILENAME) + + if "4j1b" in all_hists: + plot_stack( + all_hists["4j1b"], "4j1b", "nominal", + "png_outputs/final_stack_histogram_4j1b.png", + r"$H_T$ [GeV]", + r"$\geq$ 4 jets, 1 b-tag" + ) + + if "4j2b" in all_hists: + plot_stack( + all_hists["4j2b"], "4j2b", "nominal", + "png_outputs/stack_4j2b_nominal.png", + r"$m_{bjj}$ [GeV]", + r"$\geq$ 4 jets, $\geq$ 2 b-tags" + ) + + plot_variations(all_hists) diff --git a/reana.yaml b/reana.yaml index 8de71e6f..e0feb3a6 100644 --- a/reana.yaml +++ b/reana.yaml @@ -1,7 +1,7 @@ version: 0.8.0 inputs: files: - - ttbar_analysis_reana.ipynb + - ttbar_analysis_reana.ipynb - nanoaod_inputs.json - fix-env.sh - corrections.json @@ -9,15 +9,62 @@ inputs: - file_merging.ipynb - final_merging.ipynb - prepare_workspace.py - + - cabinetry_config.yml + - cabinetry_fit_limit.py + - plot_final_stack.py + - datacard_by_hand.txt + - make_mirrored_down.py + - statistical_inference.py directories: - histograms - utils + - results + - combine_scripts workflow: type: snakemake resources: kerberos: true file: Snakefile + parameters: + recast: + dir: /eos/user/s/shoienko/REANA_file outputs: files: - - histograms_merged.root \ No newline at end of file + - histograms_merged.root + - png_outputs/final_stack_histogram_4j1b.png + - png_outputs/stack_4j2b_nominal.png + - png_outputs/btagging_variations_4j1b_ttbar.png + - png_outputs/jet_energy_variations_4j2b_ttbar.png + - results/limits.json + - results/limit_summary.txt + - datacard_by_hand.root + - combine_plots/impacts_r400.pdf + - combine_plots/impacts_r500.pdf + - combine_plots/impacts_r600.pdf + - combine_plots/impacts_r700.pdf + - combine_plots/impacts_r800.pdf + - combine_plots/impacts_r900.pdf + - combine_plots/impacts_r1000.pdf + - combine_plots/impacts_r1200.pdf + - combine_plots/stacked_plot_shapes_fit_b_bin4j1b_m400.png + - combine_plots/stacked_plot_shapes_fit_b_bin4j2b_m400.png + - combine_plots/stacked_plot_shapes_prefit_bin4j1b_m400.png + - combine_plots/stacked_plot_shapes_prefit_bin4j2b_m400.png + - combine_plots/stacked_plot_shapes_fit_s_bin4j1b_m400.png + - combine_plots/stacked_plot_shapes_fit_s_bin4j2b_m400.png + - combine_limits/limit_summary_400.txt + - combine_limits/limits_zprimett400.json + - combine_limits/limit_summary_500.txt + - combine_limits/limits_zprimett500.json + - combine_limits/limit_summary_600.txt + - combine_limits/limits_zprimett600.json + - combine_limits/limit_summary_700.txt + - combine_limits/limits_zprimett700.json + - combine_limits/limit_summary_800.txt + - combine_limits/limits_zprimett800.json + - combine_limits/limit_summary_900.txt + - combine_limits/limits_zprimett900.json + - combine_limits/limit_summary_1000.txt + - combine_limits/limits_zprimett1000.json + - combine_limits/limit_summary_1200.txt + - combine_limits/limits_zprimett1200.json diff --git a/results/limits.json b/results/limits.json new file mode 100644 index 00000000..d62952f4 --- /dev/null +++ b/results/limits.json @@ -0,0 +1,15 @@ +{ + "poi": "zprimett400_norm", + "observed_95": 2.147694845203476, + "expected_median_95": 2.7384900925930324, + "expected_bands_95": [ + 1.4525180515511902, + 1.9579353854882438, + 2.7384900925930324, + 3.8776616683576597, + 5.360971787695893 + ], + "AGC_rebinning_applied": true, + "note": "cabinetry.fit.limit with stronger rebin (merge 4->1)", + "status": "OK" +} \ No newline at end of file diff --git a/results/plot_limits.py b/results/plot_limits.py new file mode 100644 index 00000000..50c387ea --- /dev/null +++ b/results/plot_limits.py @@ -0,0 +1,121 @@ +#!/usr/bin/env python3 +import os, re, glob, json, argparse +import numpy as np +import matplotlib.pyplot as plt + +def parse_summary(path): + """Parse POI, Observed, Expected(median) from a text summary.""" + poi = obs = exp_med = None + with open(path) as f: + for line in f: + s = line.strip() + if s.startswith("POI:"): + poi = s.split(":", 1)[1].strip() + elif s.startswith("Observed 95% CL:"): + try: obs = float(s.split(":", 1)[1]) + except: obs = None + elif s.startswith("Expected (median) 95% CL:"): + try: exp_med = float(s.split(":", 1)[1]) + except: exp_med = None + return poi, obs, exp_med + +def mass_from(fname, poi): + m = re.search(r"limit_summary[_-](\d+)", os.path.basename(fname)) + if m: return int(m.group(1)) + if poi: + m = re.search(r"zprimett(\d+)", poi) + if m: return int(m.group(1)) + return None + +def load_bands(mass, dirs): + """Try reading full expected bands [−2σ, −1σ, median, +1σ, +2σ] from JSON.""" + cands = [] + for d in dirs: + d = d.strip() + cands += glob.glob(os.path.join(d, f"limits_zprimett{mass}.json")) + cands += glob.glob(os.path.join(d, f"limits_zprimett{mass}.txt")) + cands += glob.glob(os.path.join(d, f"limits_{mass}.json")) + cands += glob.glob(os.path.join(d, f"limits_{mass}.txt")) + for p in cands: + try: + with open(p) as f: + obj = json.load(f) + arr = obj.get("expected_bands_95") or obj.get("expected") + if isinstance(arr, list) and len(arr) >= 5: + return [float(x) for x in arr[:5]] + except: + pass + return None + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--summaries_glob", default="limit_summary_*.txt") + ap.add_argument("--search_json_dirs", default=".,results") + ap.add_argument("--xlabel", default=r"$m_{Z'}$ [GeV]") + ap.add_argument("--ylabel", default=r"95% CL upper limit on $\mu$") + ap.add_argument("--out", default="limits_summary.png") + ap.add_argument("--title", default="") + ap.add_argument("--logy", action="store_true") + args = ap.parse_args() + + search_dirs = args.search_json_dirs.split(",") + + rows = [] + for p in sorted(glob.glob(args.summaries_glob)): + poi, obs, exp_med = parse_summary(p) + m = mass_from(p, poi) + if m is None: + print(f"[WARN] skip {p}: no mass") + continue + bands = load_bands(m, search_dirs) + rows.append((m, obs, exp_med, bands)) + if not rows: + raise SystemExit("No inputs found.") + + rows.sort(key=lambda x: x[0]) + masses = np.array([r[0] for r in rows], float) + obs = np.array([r[1] if r[1] is not None else np.nan for r in rows], float) + exp_med = np.array([r[2] if r[2] is not None else np.nan for r in rows], float) + bands = [r[3] for r in rows] + have_bands = any(b is not None for b in bands) + + plt.figure(figsize=(8, 5.2)) + + if have_bands: + exp_m2 = np.array([b[0] if b else np.nan for b in bands], float) + exp_m1 = np.array([b[1] if b else np.nan for b in bands], float) + exp_p1 = np.array([b[3] if b else np.nan for b in bands], float) + exp_p2 = np.array([b[4] if b else np.nan for b in bands], float) + plt.fill_between(masses, exp_m2, exp_p2, alpha=0.6, color="yellow", label=r"Exp. $\pm2\sigma$") + plt.fill_between(masses, exp_m1, exp_p1, alpha=0.8, color="lime", label=r"Exp. $\pm1\sigma$") + + plt.plot(masses, exp_med, "--", color="black", lw=2, label="Expected") + plt.plot(masses, obs, "-", color="black", lw=2, label="Observed") + + plt.xlabel(args.xlabel) + plt.ylabel(args.ylabel) + if args.title: plt.title(args.title) + if args.logy: + plt.yscale("log") + finite = np.concatenate([np.nan_to_num(obs, nan=np.inf), + np.nan_to_num(exp_med, nan=np.inf)]) + if have_bands: + finite = np.concatenate([finite, + np.nan_to_num(exp_m2, nan=np.inf), + np.nan_to_num(exp_p2, nan=np.inf)]) + finite = finite[np.isfinite(finite) & (finite > 0)] + if finite.size: + plt.ylim(finite.min()/1.8, finite.max()*1.8) + + # legend order: ±2σ, ±1σ, Expected, Observed + h, l = plt.gca().get_legend_handles_labels() + order = [i for name in [r"Exp. $\pm2\sigma$", r"Exp. $\pm1\sigma$", "Expected", "Observed"] if name in l for i in [l.index(name)]] + plt.legend([h[i] for i in order], [l[i] for i in order], loc="best", frameon=False) + + plt.grid(True, which="both", ls=":", alpha=0.4) + plt.tight_layout() + plt.savefig(args.out, dpi=200) + print(f"[OK] saved {args.out}") + +if __name__ == "__main__": + main() diff --git a/run.sh b/run.sh new file mode 100644 index 00000000..d8774581 --- /dev/null +++ b/run.sh @@ -0,0 +1,34 @@ +#!/usr/bin/env bash + +WORKFLOWNAME=$1 + +# Start worklow +reana-client run -w $WORKFLOWNAME + +while :; do + + # Wait until the execution finishes or fails + while :; do + status=$(reana-client status -w $WORKFLOWNAME --json | jq -Sr '.[].status') + if [ "$status" == "queued" ] || [ "$status" == "pending" ] || [ "$status" == "running" ]; then + sleep 15 + else + break + fi + done + + if [ "$status" == "finished" ]; then + # Success; we can stop + break + else + # Some samples failed; remove those and restart workflow (ANALYSIS SPECIFIC REQUIREMENTS) + reana-client ls -w $WORKFLOWNAME | grep "histograms/hist_result_" | awk '{if ( $2 == 0 ) print $1;}' > errors.txt + cat errors.txt + while read -r badsample; do + reana-client rm -w $WORKFLOWNAME "$badsample" + done 1 else "" + opts = "" + if setpars: + opts += f" --setParameters {setpars}" + if freeze: + opts += f" --freezeParameters {freeze}" + return opts + +def write_text_summary(json_path, m, out_txt): + with open(json_path) as f: + obj = json.load(f) + + rec = next(iter(obj.values())) + obs = rec.get("Observed", None) + exp = rec.get("Expected", {}) + exp_median = exp.get("50.0", None) + with open(out_txt, "w") as g: + g.write(f"POI: zprimett{m}\n") + if obs is not None: + g.write(f"Observed 95% CL: {obs}\n") + if exp_median is not None: + g.write(f"Expected (median) 95% CL: {exp_median}\n") + +def main(): + Path("combine_plots").mkdir(parents=True, exist_ok=True) + Path("combine_limits").mkdir(parents=True, exist_ok=True) + + os.environ.setdefault("CMSSW_BASE", ".") + os.environ.setdefault("SCRAM_ARCH", ".") + + run("python3 make_mirrored_down.py") + + maps = " ".join([f"--PO 'map=.*/zprimett{m}:r{m}[1,0,3]'" for m in MASSES]) + run(f"text2workspace.py datacard_by_hand.txt " + f"-P HiggsAnalysis.CombinedLimit.PhysicsModel:multiSignalModel {maps}") + + pois_csv = ",".join([f"r{m}" for m in MASSES]) + run(f"combineTool.py -M Impacts -d datacard_by_hand.root " + f"--redefineSignalPOIs {pois_csv} --robustFit 1 --doInitialFit -m 125", check=False) + run(f"combineTool.py -M Impacts -d datacard_by_hand.root " + f"--redefineSignalPOIs {pois_csv} --robustFit 1 --doFits -m 125", check=False) + run("combineTool.py -M Impacts -d datacard_by_hand.root --robustFit 1 --output impacts.json -m 125", check=False) + for m in MASSES: + run(f"plotImpacts.py -i impacts.json -o combine_plots/impacts_r{m} --POI r{m}", check=False) + + for m in MASSES: + tag = f"m{m}" + poi = f"r{m}" + onepoi = f"--redefineSignalPOIs {poi}" + freeze = freeze_other_pois(m) + + run(f"combine -M FitDiagnostics datacard_by_hand.root {onepoi}{freeze} " + f"-n _{tag} --saveShapes -m 125 --cminDefaultMinimizerStrategy 0", check=True) + + for region in ["bin4j1b", "bin4j2b"]: + for shape in ["shapes_prefit", "shapes_fit_b", "shapes_fit_s"]: + run("python3 combine_scripts/postFitPlot_new.py " + f"--input_file fitDiagnostics_{tag}.root " + f"--shape_type {shape} --region {region} --extra_suffix _{tag}", check=True) + + + run(f"combine -M AsymptoticLimits datacard_by_hand.root {onepoi}{freeze} " + f"-n _{tag} -m 125", check=True) + + al_root = f"higgsCombine_{tag}.AsymptoticLimits.mH125.root" + if not os.path.exists(al_root): + alt = f"higgsCombine{tag}.AsymptoticLimits.mH125.root" + if os.path.exists(alt): + al_root = alt + + if os.path.exists(al_root): + + out_json = f"combine_limits/limits_zprimett{m}.json" + run(f"combineTool.py -M CollectLimits {al_root} -o {out_json}", check=True) + + out_txt = f"combine_limits/limit_summary_{m}.txt" + write_text_summary(out_json, m, out_txt) + + + run(f"combine -M MultiDimFit datacard_by_hand.root {onepoi}{freeze} " + f"-n .{tag}.snapshot --rMin 0 --rMax 2 --saveWorkspace -m 125") + run(f"combine -M MultiDimFit higgsCombine.{tag}.snapshot.MultiDimFit.mH125.root " + f"-n .{tag} --rMin 0 --rMax 2 --algo grid --points 120 --snapshotName MultiDimFit -m 125") + run(f"combine -M MultiDimFit higgsCombine.{tag}.snapshot.MultiDimFit.mH125.root " + f"-n .{tag}.freezeAll --rMin 0 --rMax 2 --algo grid --points 800 " + f"--snapshotName MultiDimFit --freezeParameters allConstrainedNuisances -m 125") + run("python3 combine_scripts/plot1DScan.py " + f"higgsCombine.{tag}.MultiDimFit.mH125.root " + f"--others 'higgsCombine.{tag}.freezeAll.MultiDimFit.mH125.root:FreezeAll:2' " + f"--POI {poi} " + f"-o combine_plots/likelihood_scan_{tag} --breakdown Syst,Stat") + + print("\n[OK] Done. Outputs in combine_plots/ and combine_limits/") + +if __name__ == "__main__": + main() diff --git a/ttbar_analysis_reana.ipynb b/ttbar_analysis_reana.ipynb index 2aa3ee63..dcc90d9e 100644 --- a/ttbar_analysis_reana.ipynb +++ b/ttbar_analysis_reana.ipynb @@ -26,7 +26,8 @@ "\n", "import utils # contains code for bookkeeping and cosmetics, as well as some boilerplate\n", "\n", - "logging.getLogger(\"cabinetry\").setLevel(logging.INFO)" + "logging.getLogger(\"cabinetry\").setLevel(logging.INFO)\n", + "\n" ] }, { @@ -48,7 +49,7 @@ "### ML-INFERENCE SETTINGS\n", "\n", "# enable ML inference\n", - "USE_INFERENCE = False \n", + "USE_INFERENCE = False\n", "\n", "# enable inference using NVIDIA Triton server\n", "USE_TRITON = False\n" @@ -400,7 +401,8 @@ " for channel, histogram in all_histograms[\"hist_dict\"].items():\n", " for sample in histogram.axes[1]:\n", " for variation in histogram[:, sample, :].axes[1]:\n", - " variation_string = \"\" if variation == \"nominal\" else f\"_{variation}\"\n", + " # variation_string = \"\" if variation == \"nominal\" else f\"_{variation}\"\n", + " variation_string = f\"_{variation}\"\n", " if sum(histogram[:, sample, variation].values()) != 0:\n", " # only save histograms containing events\n", " # many combinations are not used (e.g. ME var for W+jets)\n", diff --git a/utils/file_input.py b/utils/file_input.py index d56c8125..c90dab72 100644 --- a/utils/file_input.py +++ b/utils/file_input.py @@ -46,6 +46,25 @@ def construct_fileset(n_files_max_per_sample, use_xcache=False, af_name="", loca "single_top_t_chan": (36.993 + 22.175)/0.252, # scale from lepton filter to inclusive "single_top_tW": 37.936 + 37.906, "wjets": 61457 * 0.252, # e/mu+nu final states + "zprimett1000": 3.528e+00, + "zprimett1200": 1.749e+00, + "zprimett2000": 1.951e-01, + "zprimett2500": 4.743e-02, + "zprimett3000": 1.494e-02, + "zprimett3500": 5.097e-03, + "zprimett400": 2.547e+01, + "zprimett4000": 1.900e-03, + "zprimett4500": 7.521e-04, + "zprimett500": 2.559e+01, + "zprimett5000": 3.193e-04, + "zprimett600": 1.809e+01, + "zprimett6000": 6.167e-05, + "zprimett700": 1.144e+01, + "zprimett7000": 1.140e-05, + "zprimett800": 7.736e+00, + "zprimett8000": 1.839e-06, + "zprimett900":5.181e+00, + "zprimett9000": 1.993e-07, # cross section after matching in pb "data": None } diff --git a/utils/file_output.py b/utils/file_output.py index 223488ec..64475926 100644 --- a/utils/file_output.py +++ b/utils/file_output.py @@ -1,46 +1,36 @@ import uproot -def save_histograms(hist_dict, fileset, filename, channel_names, add_offset=False): - nominal_samples = [sample for sample in fileset.keys() if "nominal" in sample] - +def save_histograms(hist_dict, filename, add_offset=False): with uproot.recreate(filename) as f: - out_dict = {} - for channel in channel_names: - current_hist = hist_dict[channel] - + # save all available histograms to disk + for channel, histogram in hist_dict.items(): # optionally add minimal offset to avoid completely empty bins - # (useful for the ML validation variables that would need binning adjustment to avoid those) + # (useful for the ML validation variables that would need binning adjustment + # to avoid those) if add_offset: - current_hist += 1e-6 - - out_dict[f"{channel}_pseudodata"] = ((current_hist[:, "ttbar", "ME_var"] + current_hist[:, "ttbar", "PS_var"]) / 2 - + current_hist[:, "wjets", "nominal"]) - - for sample in nominal_samples: - sample_name = sample.split("__")[0] - out_dict[f"{channel}_{sample_name}"] = current_hist[:, sample_name, "nominal"] - - # b-tagging variations - for i in range(4): - for direction in ["up", "down"]: - variation_name = f"btag_var_{i}_{direction}" - out_dict[f"{channel}_{sample_name}_{variation_name}"] = current_hist[:, sample_name, variation_name] - - # jet energy scale variations - for variation_name in ["pt_scale_up", "pt_res_up"]: - out_dict[f"{channel}_{sample_name}_{variation_name}"] = current_hist[:, sample_name, variation_name] - - # ttbar modeling - ttbar_variations = ["ME_var", "PS_var", "scaleup", "scaledown"] - for var in ttbar_variations: - out_dict[f"{channel}_ttbar_{var}"] = current_hist[:, "ttbar", var] - - # W+jets scale - wjets_variations = ["scale_var_down", "scale_var_up"] - for var in wjets_variations: - out_dict[f"{channel}_wjets_{var}"] = current_hist[:, "wjets", var] - - # write to file - for key in out_dict.keys(): - f[key] = out_dict[key] + histogram += 1e-6 + # reference count for empty histogram with floating point math tolerance + empty_hist_yield = histogram.axes[0].size*(1e-6)*1.01 + else: + empty_hist_yield = 0 + + for sample in histogram.axes[1]: + for variation in histogram[:, sample, :].axes[1]: + variation_string = "" if variation == "nominal" else f"_{variation}" + current_1d_hist = histogram[:, sample, variation] + + if sum(current_1d_hist.values()) > empty_hist_yield: + # only save histograms containing events + # many combinations are not used (e.g. ME var for W+jets) + f[f"{channel}_{sample}{variation_string}"] = current_1d_hist + + # add pseudodata histogram if all inputs to it are available + if ( + sum(histogram[:, "ttbar", "ME_var"].values()) > empty_hist_yield + and sum(histogram[:, "ttbar", "PS_var"].values()) > empty_hist_yield + and sum(histogram[:, "wjets", "nominal"].values()) > empty_hist_yield + ): + f[f"{channel}_pseudodata"] = ( + histogram[:, "ttbar", "ME_var"] + histogram[:, "ttbar", "PS_var"] + ) / 2 + histogram[:, "wjets", "nominal"] \ No newline at end of file diff --git a/utils/genXsec_cfg.py b/utils/genXsec_cfg.py new file mode 100644 index 00000000..d2119e8a --- /dev/null +++ b/utils/genXsec_cfg.py @@ -0,0 +1,21 @@ +import FWCore.ParameterSet.Config as cms +from FWCore.ParameterSet.VarParsing import VarParsing +options = VarParsing ('analysis') +options.parseArguments() +process = cms.Process('XSec') + +process.maxEvents = cms.untracked.PSet( + input = cms.untracked.int32(options.maxEvents) +) + +process.load('FWCore.MessageService.MessageLogger_cfi') +process.MessageLogger.cerr.FwkReport.reportEvery = 100000 + +secFiles = cms.untracked.vstring() +process.source = cms.Source ("PoolSource", + fileNames = cms.untracked.vstring(options.inputFiles), + secondaryFileNames = secFiles) +process.xsec = cms.EDAnalyzer("GenXSecAnalyzer") + +process.ana = cms.Path(process.xsec) +process.schedule = cms.Schedule(process.ana)